Tomography is a process that relies upon a selected form of energy being directed toward and passing through an object at more than one angle, and permits the construction of detailed images of internal structures of the object. The energy from the various angles is detected and corresponding data processed to provide a tomographic image. The received signals typically are less intense (for example, are darker) where the object is thicker or more dense, and more intense (for example, brighter) where the object is thinner or less dense.
A signal received by a single energy sensor (for example, at one angle) does not contain sufficient information to generate either a two-dimensional or a three-dimensional representation of internal structures of the object. Signals received by energy sensors arranged in a plane or volume provide sufficient information to generate a three-dimensional representation of internal structures of the object.
Tomography can be used in a variety of imaging systems with different types of transmitted and received electromagnetic radiation. In particular, in X-ray Computed Axial Tomography (CAT, or CT), X-ray radiation is projected through an object, typically at a variety of angles, and a variety of X-ray receivers, at a corresponding variety of angles, are used to receive the X-rays transmitted through the object. A computer is used to generate an image of internal structures of the object in three dimensions from signals received by the variety of X-ray receivers.
X-rays tend to pass through the object in straight lines with relatively little attenuation, allowing non-invasive capture of certain anatomical features at high resolution (for example, distinguishing features as small as 50-100 μm in one or more dimensions). X-ray CAT imaging systems can be used to image bones, organs, blood vessels, and tumors of a particular subject. While X-ray CAT imaging is able to provide high resolution of certain anatomical structures, it is relatively limited in its ability to detect, distinguish, or quantify specific chemical or biological species in the subject. Therefore, existing X-ray CAT systems cannot provide functional (or, “molecular”) information about a subject or disease state at the cellular or molecular level.
Imaging techniques such as X-ray CAT, magnetic resonance imaging (MRI) and ultrasound (US) primarily rely on physical parameters such as absorption, scattering, proton density, and relaxation rates as the primary source of contrast for imaging. Specific molecular information with these modalities cannot often be obtained or is limited. Optical imaging, for example, optical tomographic imaging, uses specific molecular activity or alterations as the source of image contrast and therefore, can provide much more molecular or functional information about a subject or disease state than imaging techniques such as X-ray CAT that primarily capture anatomical information based on physical parameters.
Optical tomographic systems use one or more wavelengths of visible or invisible light, rather than X-rays. Unlike X-ray tomography, in which X-rays tend to pass through an object in a straight line with relatively little attenuation, visible and invisible (ultraviolet or infrared) light tends to be absorbed and to scatter when passing though an object. Therefore, light does not travel in straight lines when passing through the object. Light also tends to be absorbed and scattered more when passing through a relatively thick and/or non-homogeneous medium, than when passing through a relatively thin and/or homogeneous medium.
Most conventional optical tomography systems use near infrared (near-IR, NIR) light, instead of light in the visible spectrum when passing through animal tissues, since NIR tends to be absorbed less and to scatter less than visible light. The use of NIR light generally provides the ability to image deeper tissues, for example, thicker tissues, and/or the ability to image with higher sensitivity than the use of visible light.
While optical tomography is well suited to providing molecular/functional information about a subject, the achievable resolution is not as high as with X-ray CAT or MRI. Two exemplary optical tomographic techniques are Diffuse Optical Tomography (DOT) and Fluorescence Molecular Tomography (FMT). Both DOT and FMT allow optical tomographic imaging of the internal structure of animal and/or human subjects.
DOT is an imaging technique capable of providing biological functional information by imaging hemoglobin concentration and tissue oxygenation state. DOT approaches are currently being used to detect certain types of tumors, including breast tumors.
Unlike most DOT approaches, FMT uses fluorescent molecular probes, which absorb light propagating inside of an object and emit light at a longer wavelength (lower energy) than the absorbed light inside of the object, allowing non-invasive, in vivo investigation of functional and molecular signatures in whole tissues of animals and humans. FMT systems enable molecular imaging, for example, FMT can be used to visually indicate molecular abnormalities that are the basis of a disease, rather than just imaging the anatomical structures in the area of suspected molecular, abnormalities, as with conventional imaging approaches. Specific imaging of molecular targets provides earlier detection and characterization of a disease, as well as earlier and direct molecular assessment of treatment efficacy. An illustrative FMT system is described in U.S. Patent Application Publication No. US2004/0015062, the text of which is incorporated by reference herein, in its entirety.
Most existing DOT and FMT systems use light sources and light sensors in direct contact with the object to be imaged and/or use optical matching fluid. For both DOT and FMT systems, the use of fiber guides and/or optical matching fluids limits the tomographic capacity of such systems and impedes their practicality in research and/or clinical settings.
Recent improvements in fluorescence molecular tomography have led to the development of more versatile imaging techniques that do not require either direct contact or optical contact between the light sources/detectors and the object to be imaged. These techniques employ more powerful algorithms that account for heterogeneities of the index of refraction within and surrounding the animal tissue which give rise to photon reflections at the boundaries. See, for example, International (PCT) Application Publication No. WO 03/102558, published 11 Dec. 2003; and R. Schulz, J. Ripoll and V. Ntziachristos, “Experimental Fluorescence Tomography of Tissues with Noncontact Measurements,” IEEE Transactions on Medical Imaging, Vol. 23, No. 4, pp. 492-500 (2004), the texts of which are incorporated herein by reference in their entirety. These techniques are further augmented by the use of so-called free-space transformations, which take into account the presence of a non-turbid medium (air) between the object to be imaged and the detectors. See, for example, International (PCT) Application Publication No. WO 2004/072906, published 26 Aug. 2004; and J. Ripoll, R. Schulz and V. Ntziachristos, “Free-Space Propagation of Diffuse Light: Theory and Experiments,” Physical Review Letters, Vol. 91 No. 10 (2003), the texts of each of which are incorporated herein by reference in their entirety.
Multi-modality tomographic imaging is emerging as an increasingly important tool in pre-clinical and clinical imaging, as it allows the combination of complementary image datasets, for example, from Fluorescence Molecular Tomography (FMT), Magnetic Resonance Imaging (MRI or MR), Computed Axial Tomography (CAT or CT), Positron Emission Tomography (PET), and others, to indicate, highlight and correlate specific biological processes with morphological or functional information.
Co-registering image datasets for a given subject that are obtained from different modalities may be quite difficult because it is normally necessary to move the subject from one imaging system to another, and movement of the subject often causes complex misalignment of the datasets because the subject is not a rigid body. One approach to solving this problem is a hardware-based approach that involves a complex architecture of sources and detectors from two or more modalities within a single rotating gantry. A second approach is a software approach that involves mathematically advanced image transformation algorithms to allow the fusion of image datasets from the different imaging modalities into a single integrated dataset. The primary limitation of the hardware approach is the complexity and cost associated with multi-modality gantries. The primary limitation of the software approach resides in the relatively inferior image fusion results due to the softness or non-rigidity of biological tissue as it is transported from one imaging modality to another. Thus, there exists a need for new technologies and methods to enable the simple and accurate registration of data sets across optical, X-ray, magnetic resonance, nuclear or other tomographic modalities that overcome the limitations of existing solutions.